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Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review
Objective To assess the methodological quality of studies on prediction models developed
using machine learning techniques across all medical specialties. Design Systematic …
using machine learning techniques across all medical specialties. Design Systematic …
The promise of clinical decision support systems targetting low-resource settings
Low-resource clinical settings are plagued by low physician-to-patient ratios and a shortage
of high-quality medical expertise and infrastructure. Together, these phenomena lead to …
of high-quality medical expertise and infrastructure. Together, these phenomena lead to …
Prediction of general medical admission length of stay with natural language processing and deep learning: a pilot study
Length of stay (LOS) and discharge destination predictions are key parts of the discharge
planning process for general medical hospital inpatients. It is possible that machine …
planning process for general medical hospital inpatients. It is possible that machine …
Improving length of stay prediction using a hidden Markov model
Estimating length of stay of intensive care unit patients is crucial to reducing health care
costs. This can help physicians intervene at the right time to prevent adverse outcomes for …
costs. This can help physicians intervene at the right time to prevent adverse outcomes for …
Explainable Deep Contrastive Federated Learning System for Early Prediction of Clinical Status in-Intensive Care Unit
Early identification of patients' clinical status plays a critical role in intensive care unit (ICU)
care. The increased adoption of electronic health records (EHRs) in the ICU creates …
care. The increased adoption of electronic health records (EHRs) in the ICU creates …
Translational artificial intelligence-led optimization and realization of estimated discharge with a supportive weekend interprofessional flow team (TAILORED-SWIFT)
Weekend discharges occur less frequently than discharges on weekdays, contributing to
hospital congestion. Artificial intelligence algorithms have previously been derived to predict …
hospital congestion. Artificial intelligence algorithms have previously been derived to predict …
Security risk assessment of healthcare web application through adaptive neuro-fuzzy inference system: a design perspective
Introduction The imperative need for ensuring optimal security of healthcare web
applications cannot be overstated. Security practitioners are consistently working at …
applications cannot be overstated. Security practitioners are consistently working at …
A comparative study of machine learning models for predicting length of stay in hospitals
There has been a growing interest in recent years in correctly predicting the Length of Stay
(LoS) in a hospital setting. Estimating the LoS on patient'admission helps hospitals in …
(LoS) in a hospital setting. Estimating the LoS on patient'admission helps hospitals in …
Predicting length of stay of coronary artery bypass grafting patients using machine learning
Background There is a growing need to identify which bits of information are most valuable
for healthcare providers. The aim of this study was to search for the highest impact variables …
for healthcare providers. The aim of this study was to search for the highest impact variables …
Predicting length of stay for trauma and emergency general surgery patients
Background Predicting length of stay (LOS) is difficult for trauma and emergency general
surgery (TEGS) patients. Our aim was to determine the accuracy of LOS predictions by …
surgery (TEGS) patients. Our aim was to determine the accuracy of LOS predictions by …